As a Senior Machine Learning Engineer at Iterable, you will design and build end-to-end machine learning systems that power intelligence across our product.
Responsibilities
Build and ship core product ML capabilities including send-time/frequency optimization, recommendation engines, and campaign personalization.
Own the full model lifecycle end-to-end: EDA, feature engineering, distributed training, production deployment, latency monitoring, and online experimentation (e.g., multi-armed bandits, ensemble models).
Build and scale our data pipelines and serving architectures using Databricks, Spark, AWS, Ray, and Kubernetes.
Partner with Backend and Platform teams to elevate feature serving, improve overall data architecture, and establish engineering standards across the team.
Mentor engineers on the team and help set the technical direction for ML at Iterable.
Lead end‑to‑end deployments of AI models in life sciences organizations, designing production systems and ensuring regulatory compliance.
Responsibilities
Design and ship production systems around models, owning integrations, data provenance, reliability, and on‑call readiness across research, clinical, and operational workflows.
Lead discovery and scoping from pre‑sales through post‑sales, translating ambiguous workflow needs into hypothesis‑driven problem framing, system requirements, and an execution plan with measurable endpoints.
Define and enforce launch criteria for regulated contexts, including validation evidence, audit readiness, outcome metrics, and drive delivery until sustained production impact is demonstrated.
Build in sensitive scientific data environments where auditability, validation, and access controls shape architecture, operating procedures, and failure handling.
Run evaluation loops that measure model and system quality against workflow‑specific scientific benchmarks and use results to drive model and product changes.
Requirements
PhD
MS
equivalent applied experience in a life sciences relevant field
The Senior Machine Learning Engineer (L5) will own end-to-end initiatives across the ML lifecycle, from problem formulation, feature engineering, and modeling to production serving and online A/B experimentation.
Compensation
Annual base salary range (excluding equity and bonus): $213,750—$225,000 USD
Responsibilities
Own end-to-end machine learning initiatives from problem formulation and offline evaluation to production deployment and online A/B testing
Build and iterate on multi-stage recommendation, retrieval, and contextual bandit models
Design intelligent notification orchestration, candidate ranking, and targeting systems
Develop production LLM pipelines, grounding workflows, and automated evaluation loops
Architect resilient, low-latency model serving services and real-time feature pipelines
Architecting, developing, and supporting enterprise-scale Agentic AI solutions using LLMs and RAG pipelines.
Compensation
Salary Range: $121,350.00 - $199,365.00
Responsibilities
Architecting, developing, deploying, and supporting enterprise-scale Agentic AI solutions
Build and implement scalable AI systems using Large Language Models (LLMs), retrieval-augmented generation (RAG) pipelines, model orchestration frameworks, Agentic frameworks, and secure API solutions
Ensure AI solutions align with enterprise security, governance, and compliance requirements
Collaborate cross-functionally with data scientists, software engineers, infrastructure teams, product teams, and business partners
Evaluate emerging technologies and contribute to enterprise AI roadmap
Seeking a Machine Learning Engineer/AI Researcher/Data Scientist to establish evidence for safer AI by designing safety benchmarks and experiments for multimodal large language models.
Compensation
The expected base salary ranges for this position are: 184,000 USD – 287,500 USD for Level 4; and 224,000 USD – 356,500 USD for Level 5.
Responsibilities
Design and maintain safety benchmarks for large language and multimodal models
Define metrics and protocols that assess safety alongside model usefulness
Build reusable Python pipelines with versioned datasets and experiment tracking
Develop tools to discover failure modes through automated red-teaming
Investigate model failures and develop regression tests
Evaluate and prioritize AI initiatives based on business value, technical feasibility, risk, and scalability
Track and communicate AI portfolio progress, investment, realized benefits, and risk
Partner with the Corporate AI Enablement Center on governance for AI solutions
Requirements
Bachelor's degree in Engineering, Computer Science, Data Science, Information Systems, a related field, or a combination of education and equivalent experience.
Nice to have: Experience in industrial manufacturing, aerospace, or another regulated engineering environment
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